Object segmentation is often needed in medical image analysis, e.g., for surgery planning or for quantitative measurements of vessel stenosis. Although there are many segmentation methods in the literature, many of them fail to give satisfactory result when the image resolution is limited or has relatively high noise content. These difficulties can sometimes be overcome by user intervention, but this often results in complicated workflows and long analysis time. The level-set method, invented in the 1980s, is for example known for use as an image segmentation tool, due to its ability to automatically handle complicated topological changes of the targeted object, keep the contour smooth on a noisy background and generate segmentation results with sub-voxel accuracy. For an introduction see e.g. Osher, S., and Fedkiw, R. P., “Level set methods and dynamic implicit surfaces”, Springer (2003). Many studies have shown that the level-set technique improves the segmentation accuracy and reduces the need for user supervision, such as Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., and Gerig, G., “User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability,” Neuroimage 31(3), 1116-1128 (2006), and Manniesing, R., Velthuis, B. K., Van Leeuwen, M. S., Van der Schaaf, I. C., Van Laar, P. J., and Niessen, W. J., “Level set based cerebral vasculature segmentation and diameter quantification in CT angiography,” Medical Image Analysis 10(2), 200-214 (2006). Despite the success and robustness achieved in various experimental environments, its application in clinical practice is still rather limited, mainly due to the long computation time. Several methods have been proposed to accelerate the level-set method; for example Sethian et al invented the narrow-band method, in which the computation of the level-set function is limited to a band a few pixels wide instead of the whole image, see Adalsteinsson, D., Adalsteinsson, D., Sethian, J. A., and Sethian, J. A., “A Fast Level Set Method for Propagating Interfaces,” JOURNAL OF COMPUTATIONAL PHYSICS 118, 269-277 (1994). Whitaker later extended narrow-band level set to the sparse field level set, where the narrow band is only one pixel wide and the level set function is recalculated with a distance transform in each iteration, see Whitaker, R. T., “A level-set approach to 3d reconstruction from range data,” INTERNATIONAL JOURNAL OF COMPUTER VISION 29, 203-231 (1998). However, in many applications these methods do not give satisfactory performance, as segmentation takes too long. Hence it would be desirable with a level set based image segmentation method that allows for faster segmentation. In general it would be desirable with a level set based image processing method that would allow for faster execution, that is, not only in the case of image segmentation but also in other situations where level set methods can be employed in image processing, for example when used for noise reduction.